Building Predictive Models in R Using the caret Package
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The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R to simplify model training and tuning across a wide variety of modeling techniques.Abstract:
The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.read more
Citations
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Journal ArticleDOI
Topographic Cortico-cerebellar Networks Revealed by Visual Attention and Working Memory.
James A. Brissenden,Sean M. Tobyne,David E. Osher,Emily J. Levin,Mark A. Halko,David C. Somers +5 more
TL;DR: The findings indicate that recruitment by visuospatial attentional functions within cerebellar lobule VIIb/VIIIa is highly specific, and the topographic arrangement of these functions is mirrored in frontal and parietal cortex.
Journal ArticleDOI
Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels
Hamid Ebrahimy,Mohsen Azadbakht +1 more
TL;DR: Compared against the LST derived from Landsat-8 thermal imageries, ELM required the least computational effort, and when it was combined with SVM-RFE, general efficiency of the downscaling procedure was increased substantially.
Journal ArticleDOI
Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability
TL;DR: In this article, a case study in a semiarid landscape of southeastern Arizona, USA is presented, where surface soil texture and coarse fragment classes were predicted using a 28-year time series of Landsat TM derived normalized difference vegetation index (NDVI) and modeled using support vector machine (SVM) classification, and results evaluated relative to more traditional RS approaches (e.g., mono-, bi-, and multi-temporal).
Journal ArticleDOI
Large-Scale Examination of Spatio-Temporal Patterns of Drifting Fish Aggregating Devices (dFADs) from Tropical Tuna Fisheries of the Indian and Atlantic Oceans
TL;DR: dFADs drift at sea on average for 39.5 days, with time at sea being shorter and distance travelled longer in the Indian than in the Atlantic Ocean, suggesting that 1,500-2,000 may be lost onshore each year.
Journal ArticleDOI
Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools
TL;DR: Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools.
References
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